在线症状评估应用程序,大型语言模型和非专业人士的准确性,用于自行选决策
Marvin Kopka1, Niklas von Kalckreuth2, Markus A Feufel2
1Division of Ergonomics, Department of Psychology and Ergonomics (IPA), Technische Universität Berlin, Berlin, Germany. marvin.kopka@tu-berlin.de.
NPJ digital medicine
|March 26, 2025
概括
症状评估应用程序 (SAA) 和大型语言模型 (LLM) 在自我选中显示了中度的准确性,但它们的可变性需要逐案评估,以便在医疗保健决策中安全有效地使用.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 症状评估应用程序 (SAA) 和大型语言模型 (LLM) 越来越多地用于公众的自我选.
- 现有的研究缺乏关于LLM准确性的综合证据,以及与SAA和非专业人士进行的比较分析.
研究的目的:
- 系统地审查和评估SAA和LLMs的自我选准确性.
- 将SAA和LLM的准确性与非专业人士的自我选能力进行比较.
主要方法:
- 采用了一种系统审查方法.
- 选了1549项研究,其中19项用于最终分析.
- 评估了SAA,LLM和非专业人士的自我选准确性.
主要成果:
- SAA显示中度但高度可变的自我分类准确性 (11.5-90.0%).
- 在LLM中,准确度中等 (57.8-76.0%),变化很小.
- 非专业人士显示中度准确度 (47.3-62.4%) 与低可变性.
结论:
- 目前,SAA和LLM都没有普遍推或阻止自我选.
- 应根据特定的用例和用户人口统计数据评估SAA和LLM的实用性.
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